init dump for gradio

README.md

@@ -53,3 +53,44 @@ echo "FAL_API_KEY=your_fal_api_key_here" >> .env
 ```
 
 These API keys are required for certain features of the application to work properly.
+
+# Face Enhancement Gradio Demo
+
+A web interface for the face enhancement workflow using Gradio.
+
+## Features
+
+- Simple web interface for face enhancement
+- Upload input image and reference face image
+- Queue system to process jobs sequentially on a single GPU
+- Approximately 60 seconds processing time per image
+
+## Setup
+
+1. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+2. Run the Gradio demo:
+
+```bash
+python gradio_demo.py
+```
+
+3. Open your browser and go to http://localhost:7860
+
+## Usage
+
+1. Upload an input image you want to enhance
+2. Upload a reference face image
+3. Click "Enhance Face" to start the process
+4. Wait approximately 60 seconds for processing
+5. View the enhanced result in the output panel
+
+## Notes
+
+- The demo uses a job queue to ensure only one job runs at a time
+- Processing takes approximately 60 seconds per image
+- Temporary files are created during processing and cleaned up afterward

gradio_demo.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import os
+import tempfile
+from main import process_face
+
+def enhance_face_gradio(input_image, ref_image):
+    """
+    Wrapper function for process_face that works with Gradio.
+    
+    Args:
+        input_image: Input image from Gradio
+        ref_image: Reference face image from Gradio
+        
+    Returns:
+        str: Path to the enhanced image
+    """
+    # Create temporary files for input, reference, and output
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as input_file, \
+         tempfile.NamedTemporaryFile(suffix=".png", delete=False) as ref_file, \
+         tempfile.NamedTemporaryFile(suffix=".png", delete=False) as output_file:
+        
+        input_path = input_file.name
+        ref_path = ref_file.name
+        output_path = output_file.name
+    
+    # Save uploaded images to temporary files
+    input_image.save(input_path)
+    ref_image.save(ref_path)
+    
+    # Process the face
+    process_face(
+        input_path=input_path,
+        ref_path=ref_path,
+        crop=False,
+        upscale=False,
+        output_path=output_path
+    )
+    
+    # Clean up temporary input and reference files
+    os.unlink(input_path)
+    os.unlink(ref_path)
+    
+    return output_path
+
+# Create the Gradio interface
+with gr.Blocks(title="Face Enhancement Demo") as demo:
+    gr.Markdown("# Face Enhancement Demo")
+    gr.Markdown("Upload an input image and a reference face image to enhance the input.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="pil")
+            ref_image = gr.Image(label="Reference Face", type="pil")
+            enhance_button = gr.Button("Enhance Face")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Enhanced Result")
+    
+    enhance_button.click(
+        fn=enhance_face_gradio,
+        inputs=[input_image, ref_image],
+        outputs=output_image,
+        queue=True  # Enable queue for sequential processing
+    )
+    
+    gr.Markdown("""
+    ## Instructions
+    1. Upload an image you want to enhance
+    2. Upload a reference face image
+    3. Click 'Enhance Face' to start the process
+    4. Processing takes about 60 seconds
+    """)
+
+# Launch the Gradio app with queue
+if __name__ == "__main__":
+    # Set up queue with max_size=20 and concurrency=1
+    demo.queue(max_size=20)  # Configure queue size
+    demo.launch(
+        share=False,  # Set to True if you want a public link
+        server_name="0.0.0.0",  # Make available on all network interfaces
+        server_port=7860,  # Default Gradio port
+        # concurrency_count=1  # Process one job at a time
+    ) 

requirements.txt

@@ -4,4 +4,6 @@ comfy-cli
 python-dotenv
 requests
 openai
-fal-client
+fal-client
+gradio>=3.50.2
+pillow>=10.0.0